계명대학교 의학도서관 Repository

Identifying fibromyalgia subgroups using cluster analysis

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Author(s)
Y.‐R. YimK.‐E. LeeD.‐J. ParkS.‐H. KimS.‐S. NahJ.H. LeeS.‐K. KimY.‐A. LeeS.‐J. HongH.‐S. KimH.‐S. LeeH.A. KimC.‐I. JoungS.‐S. Lee
Keimyung Author(s)
Kim, Sang Hyon
Department
Dept. of Internal Medicine (내과학)
Journal Title
European Journal of Pain
Issued Date
2017
Volume
21
Issue
2
Abstract
Background : Patients with fibromyalgia (FM) exhibit significant clinical heterogeneity, in terms of physical, social and psychological functions, as well as therapeutic responses. Here, we examined FM patients in terms of pain, physical, social and psychological variables to identify clinical subgroups that may be predictive of treatment patterns.
Methods : A total of 313 FM patients were interviewed using a structured questionnaire that included sociodemographic data, current or past FM symptoms and current use of relevant medications. A K‐means cluster analysis was conducted using variables reflecting tender points, the Fibromyalgia Impact Questionnaire, Beck Depression Inventory, State‐Trait Anxiety Inventor and Social Support Scale.
Results : Four distinct clusters were identified in these patients. Group 1 was characterized by high pain levels, severe physical and mental impairment and low social support. Group 2 had moderate pain and physical impairment, mild mental impairment and moderate social support. Group 3 had moderate pain, low physical and moderate mental impairment and low social support. Group 4 had low pain levels, nearly normal physical and mental function and high social support. Group 1 was more often a current or past smoker, more likely to have a variety of symptoms, including swelling, cognitive dysfunction, dizziness, syncope, oesophageal dysmotility, dyspepsia, irritable bladder, vulvodynia and restless leg syndrome.
Conclusions : We identified four subgroups of FM patients based on pain, physical, social and psychological function. These subgroups had different clinical symptoms and medication profiles, suggesting that FM may be better managed using a more comprehensive assessment of an individual patient's symptoms.
Keimyung Author(s)(Kor)
김상현
Publisher
School of Medicine
Citation
Y.‐R. Yim et al. (2017). Identifying fibromyalgia subgroups using cluster analysis. European Journal of Pain, 21(2), 374–384. doi: 10.1002/ejp.935
Type
Article
ISSN
1090-3801
Source
https://onlinelibrary.wiley.com/doi/abs/10.1002/ejp.935
DOI
10.1002/ejp.935
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/32755
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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